Data Sources Research Articles

Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.

Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.

An assessment of federal alcohol policies in Canada and priority recommendations: Results from the 3rd Canadian Alcohol Policy Evaluation Project.

To systematically assess the Canadian federal government's current alcohol policies in relation to public health best practices. The 2022 Canadian Alcohol Policy Evaluation (CAPE) Project assessed federal alcohol policies across 10 domains. Policy domains were weighted according to evidence for their relative impact, including effectiveness and scope. A detailed scoring rubric of best practices was developed and externally reviewed by international experts. Policy data were collected between June and December 2022, using official legislation, government websites, and data sources identified from previous iterations of CAPE as sources. Contacts within relevant government departments provided any additional data sources, reviewed the accuracy and completeness of the data, and provided amendments as needed. Data were scored independently by members of the research team. Final policy scores were tabulated and presented as a weighted overall average score and as unweighted domain-specific scores. Compared to public health best practices, the federal government of Canada scored 37% overall. The three most impactful domains-(1) pricing and taxation, (2) marketing and advertising controls, and (3) impaired driving countermeasures-received some of the lowest scores (39%, 10%, and 40%, respectively). Domain-specific scores varied considerably from 0% for minimum legal age policies to 100% for controls on physical availability of alcohol. Many evidence-informed alcohol policies have not been adopted, or beenadopted only partially, by the Canadian federal government. Urgent adoption of the recommended policies is needed to prevent and reduce the enormous health, social, and economic costs of alcohol use in Canada.

Intraoperative Frozen Section Evaluation of Pancreatic Specimens and Related Liver Lesions.

Frozen sections are essential in the surgical management of patients, especially those with pancreatic masses, because frozen sections can provide answers intraoperatively and aid in treatment decisions. Pancreas frozen sections are challenging because of the small tissue size, processing artifacts, neoadjuvant treatment effects, and concurrent pancreatitis-like obstructive changes. The authors present a review of intraoperative evaluation of pancreatic specimens. To provide an approach to the diagnosis of pancreatic adenocarcinoma on frozen sections and to discuss commonly encountered pitfalls. Indications for pancreas frozen sections and specific margin evaluation will be discussed. We will also review frozen section diagnosis of subcapsular liver lesions and tumors other than metastases of pancreatic ductal adenocarcinoma. Data sources included a literature review and the personal experiences of the authors. The features for diagnosis of pancreatic adenocarcinoma include disordered architecture, glands at abnormal locations, and atypical cytology. It is important to be aware of the pitfalls and clues on frozen section. The evaluation of resection margins can be challenging, and in the setting of the resection of cystic tumors, the key is the diagnosis of high-grade dysplasia or cancer. Finally, it is vital to remember the differential diagnosis for subcapsular liver lesions because not all lesions will be metastases of adenocarcinomas or bile duct adenomas. Frozen sections remain a useful tool for the intraoperative management of patients with pancreatic tumors.

Crowdsourcing RTK: a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution (AR) in precise positioning. However, traditional real-time precise positioning frameworks (i.e., NRTK and PPP-RTK) depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network. This results in limited public appeal. With the mass production of autonomous driving vehicles, more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps. In this study, we propose a new GNSS positioning framework that relies on dual base stations, massive vehicle GNSS data, and crowdsourced atmospheric delay correction maps (CAM). The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way. Specifically, the map consists of between-station single-differenced ionospheric and tropospheric delays. We introduce the whole framework of CAM initialization for individual vehicles, on-cloud CAM maintenance, and CAM-augmented user-end positioning. The map data are collected and preprocessed in vehicles. Then, the crowdsourced data are uploaded to a cloud server. The massive data from multiple vehicles are merged in the cloud to update the CAM in time. Finally, the CAM will augment the user positioning performance. This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other. We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales. We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.

Penerapan Keluarga Sakinah oleh Pasangan Suami Istri sebagai Keluarga Binaan di KUA Wonokromo Surabaya

The aim of this research is to determine the content of marriage guidance received from the Wonokromo Religious Affairs Office (KUA), to understand the reasons why prospective couples participate in marriage guidance at KUA Wonokromo, and to ascertain the practice of the values of a Sakinah family by married couples under the mentorship of KUA Wonokromo. The research method used is qualitative. The type of research utilized is a case study, which involves an in-depth, detailed, and detailed investigation into a specific event or ongoing case. The data sources obtained in this study utilize primary data sources derived from interviews. The data collection method employed by the researcher is through direct interviews with informants. The findings of the research on the content of marriage guidance received from KUA Wonokromo include: (a) Patience to better control emotions, (b) Keeping silent to avoid conflicts, (c) Discussing together for clarity and finding appropriate solutions, and (d) Calming oneself to think clearly in making decisions. Second, the reasons why prospective couples participate in marriage guidance at KUA Wonokromo include: (a) Teaching mutual understanding towards spouses, (b) Building mental readiness for the next stage, and (c) Collaborating for mutual progress. Third, the practice of the values of a Sakinah family by married couples under the mentorship of KUA Wonokromo includes: (a) Building a harmonious family, (b) Being prepared to enter the marriage stage, and (c) Having physical and mental knowledge for the future.

Analysis of Civil Servant Performance in Population Administration Services at the Mallusetasi Sub-District Office Barru Regency

This study aims to determine the performance of civil servants (PNS) in population administration services at the Mallusetasi Sub-District Office, Barru Regency. The study utilizes the following indicators: a. Quality, b. Quantity, c. Timeliness, d. Effectiveness. Furthermore, to identify the factors influencing PNS performance in population administration services at the Mallusetasi Sub-District Office, Barru Regency, the following indicators are employed: a. Effectiveness and efficiency, b. Authority and responsibility, c. Discipline, and d. Initiative. Data sources were obtained through observation, documentation, and interviews. The research type employed is qualitative. The results of this study indicate that the performance of Civil Servants (PNS) in population administration services at the Mallusetasi Sub-District Office, Barru Regency, is satisfactory. Moreover, based on the research indicators: effectiveness and efficiency, authority and responsibility, discipline, and initiative, it is concluded that these indicators influence the performance of Civil Servants (PNS) in population administration services at the Mallusetasi Sub-District Office, Barru Regency.

Stimulus Minat Belajar Matematika Menggunakan Media Pembelajaran Audio Visual di Tingkat Sekolah Dasar

This article describes the skills of students in learning mathematics by using media and audio in madrasah/primary schools in Ibtidaiyah. This study aims to determine the use of these communication tools on students' interest in learning mathematics. This research is qualitative research using the SLR (Systematic Literature Review) method. Data sources are 10 journal articles indexed by Google Scholar. Data analysis was carried out with the stages of data collection, data reduction, verification, and conclusion drawing. This research was conducted in elementary schools to see the use of audio and digital media in learning mathematics. The results of this study show that audio and video media can increase students' interest in learning mathematics. Students learn math through these two methods compared to traditional teaching methods. The use of audio and digital media can increase students' participation in the learning process. In addition, students can better understand difficult and complex mathematical concepts because of the access provided by both media. The results provide important implications for curriculum development at the Primary level and recommend the inclusion of audio and digital media in mathematics education. This research makes an important contribution to improving the quality of mathematics teaching and students' interest in the course. This research is also expected to provide a reference for developing new interactive teaching methods at the elementary school level.

Variations in the coherence and engagement in students’ experience of blended learning

We report a study which examines coherence of engagement of 344 first year engineering students’ blended learning experience. Using self-report and observational data sources, we demonstrate that student perceptions of the blended learning environment, academic learning outcomes, and engagement with the online learning activities are logically related at the variable level as shown by correlation analyses; and at the level of student groupings of similar learning experience and behaviors, as revealed by cluster, ANOVA, and 2 x 2 contingency analyses. Using self-report data, we found that when students perceived the learning activities in the f2f and online environments were integrated, they were more likely to be engaged with the online learning and to perform relatively higher on the assessment tasks than students who perceived disintegration between f2f and online learning. Using the observational data, students who were more engaged with the online learning tended to perceive that the online learning was well integrated with the f2f learning, that the online contributions were valuable for the whole learning experience, and achieved relatively higher than less engaged students. A 2 x 2 contingency table revealed a logical relationship between the groupings of students based on the selfreport and observational data: moderate and positive association was found between students with coherent perceptions and more engagement; and between students with fragmented perceptions and less engagement. The use of multiple data sources and methods enabled triangulation, strengthened analysis power, and offered a more comprehensive picture of students’ blended learning experience.

Simultaneous Global Drug Development and Multiregional Clinical Trials (MRCT): 5 Years After Implementation of ICH E17 Guidelines.

The ICH E17 guidelines (2014-2017) on Multiregional Clinical Trials (MRCT) was a joint effort by the regulators and industry to facilitate simultaneous global drug development and registration through taking a strategic approach for clinical trials. In other words, the objective was to reduce the time it takes to bringing medications to patients around the world through minimizing unnecessary duplication of local or regional studies, which may add the regulatory burden to cost and time of bringing new therapies to patients. Under the auspices of ICH, training materials were created and provided to various stakeholders. Despite the successful promotion of the benefits of ICH E17 MRCT guidelines across the different regions, the uptake of some concepts (e.g., pooling strategy) in the ICH E17 guidelines has been slow. This paper describes various factors which could affect the conduct of MRCT at a global level, including ambiguity in definition of "region" (in MRCT), new regulatory requirements to enroll a diverse patient population, the use of decentralized clinical trials, use of data sources other than randomized clinical trials (e.g., use of Real Word Data), and the impact of the COVID-19 pandemic on the conduct of MRCT.

Exploring a practice‐based approach to elementary preservice science teacher preparation

AbstractThe science methods course model presented here, called an immersed methods course (IMC), engaged preservice elementary teachers in reform‐based science instructional approaches within authentic classroom settings and with elementary students in a guided format. Questions guiding the study were: (1) Did participation in the IMC model impact preservice teachers (PTs') science teaching self‐efficacy? (2) Which science methods course model activities/experiences did PTs identify as the most impactful on their science teaching? (3) How did participation in the IMC model impact PTs' understandings of science teaching? Participants were 138 elementary PTs from a Southeastern United States university. Data were collected from seven course sections over 3 years and included pre/post science teaching self‐efficacy survey data, post written responses, and interviews. Member checking approaches were also used to refine and establish the validity of findings. Researchers completed qualitative data analysis, which was complimented by quantitative statistical analysis. Findings across data sources suggest participation positively influenced PTs' science teaching self‐efficacy and beliefs about the effectiveness of the instructional approaches. Participation also helped PTs apply theories to practice and develop competency using the practices. Findings highlight how the IMC model mitigates constraints associated with current science teacher preparation approaches.

Applying PEOP to Develop an Intervention Framework for Employment in Parkinson's Disease.

Employment intervention is essential for supporting people with Parkinson's disease (PwPD), who leave the workforce on average 5 years earlier than those without Parkinson's disease (PD). We developed a framework for occupational therapists (OTs) to address employment for PwPD. Our qualitative analysis employed data source triangulation of environmental scan of resources and publications, electronic medical records data, and focus groups with clinicians and PwPD. The framework underwent expert panel review by OTs and researchers who are knowledgeable of current evidence-based practices for PD. The framework is based on the Person-Environment-Occupation-Performance (PEOP) Model and PEOP Occupational Therapy Process and comprised of four parts: PEOP features of PD relevant to employment, performance changes experienced by PwPD, a process to evaluate and address employment, and intervention ideas. This framework serves as an impetus to explore evidence-based, scalable, proactive intervention approaches to address employment in PwPD.

Selecting subsets of source data for transfer learning with applications in metal additive manufacturing

Selecting subsets of source data for transfer learning with applications in metal additive manufacturing

A Human-In-One-Loop Active Domain Adaptation Framework for Digit Recognition

ABSTRACT Domain adaptation can effectively enhance a model’s performance on target domain data with limited data. However, when some target domain labels are obtainable, training the model with both source and target domain data simultaneously can lead to worse performance due to the lower density of target domain data. If a large amount of target domain data is labeled without discrimination, it will necessitate a considerable expenditure of human resources. To address this issue, this paper proposes a human-in-one-loop active domain adaptation framework based on Target Domain Feature Generation to solve the problems. The oracle participates in only one iteration of data labeling, and a target domain classifier will take over the subsequent rest iterations. An image generator based on multiple CycleGANs forms an iterative co-training mechanism, which can continuously generate more high-quality labeled fake target domain data in iterations to improve the performance of the target domain classifier. The Top-N labeled data selection method with high confidence is devised to select the most accurately predicted data for labeling, reducing manual labeling workload. This framework can achieve an average accuracy of 0.8869 on six domain pairs, doubling the classical domain adaptation method DSN, requiring only a small amount of manual labeling.

Planning for tourism under uncertainty: lessons from Macao, China (2002–2022)

ABSTRACT Drawing on the perspective of governance and planning uncertainties mentioned in Abbott 's 2005 and 2009 articles, namely, “Understanding and managing the unknown: The nature of uncertainty in planning” and “Planning for complex metropolitan regions: A better future or a more certain one?” , and several urban planning theories, this study examined the types of planning uncertainty that Macao (China), experienced from 2002 to 2020, and the planning approaches it adopted to tackle the uncertainties. A mixed research method was adopted. Fifteen qualitative face-to-face interviews with key informants were conducted, and secondary sources of data, such as government plans, reports, and statistics, were also reviewed. Results indicated that environmental, casual, value, organisational, and chance uncertainties emerged at three different stages of Macao's tourism development. To deal with these uncertainties, a variety of planning approaches were adopted, including guided flexible planning, comprehensive planning, communicative planning, and adaptive learning. This study contributed a novel perspective to examine tourism planning uncertainties over time. It emphasised the importance of a flexible planning approach in tackling uncertainties of growing complexity. The findings also shed light on how planners can strengthen their capacity for tackling uncertainty.

LT-1 SAR Satellite Constellation for Permafrost Deformation Monitoring Along the Tibetan Plateau Engineering Corridor

Abstract. The Tibetan Plateau stands as one of China's largest middle and low latitude permafrost regions. However, the effects of global warming and human activities have led to permafrost thawing, inducing surface instability and posing significant threats to infrastructure and indigenous communities. The deployment of Lu Tan-1 (LT-1), China's premier L-band synthetic aperture radar (SAR) satellite constellation, offers a novel opportunity to assess these changes. This paper evaluates the deformation of critical engineering corridors, such as the Qinghai-Tibet Railway (QTR) and the Qinghai-Tibet Highway (QTH), utilizing time-series InSAR techniques with LT-1 SAR constellation data. We introduce both Stacking InSAR and a multi-baseline persistent scatterer multitemporal (MT-InSAR) method to enhance permafrost and engineering corridor deformation detection capabilities. Results obtained through the MT-InSAR approach reveal line-of-sight (LOS) deformation velocities of permafrost in the Beiluhe region ranging from -90 mm/y to approximately 70 mm/y, with an average velocity amplitude of 0.06 m/y. Differential displacement between alpine meadows and alpine deserts across the Beiluhe region is successfully discerned using LT-1 SAR data. Deformation velocities of QTR, QTH were found to be lower than that of permafrost, with average velocities of 0.027 m/y. These findings underscore the LT-1 SAR constellation's potential to serve as a valuable SAR data source for monitoring engineering corridor deformation within the Tibetan Plateau permafrost region.

An Approach to the Systematic Characterization of Multitask Accelerated CNN Inference in Edge MPSoCs

Deep Learning is ubiquitous today and is increasingly moving from the cloud down to the edge of networked infrastructures, where it enables embedded applications to perform complex inference tasks close to the data sources, reducing long-distance data movement and alleviating the need for a powerful cloud infrastructure. Edge-class multi-processor system on chip (MPSoC) devices featuring an on-chip FPGA fabric offer key advantages for Deep Learning inference tasks, especially for complex applications where multiple models may be run concurrently in the same platform. In this work, we propose an approach and a practical framework for the systematic characterization of multithreaded Deep Learning inference on edge FPGA MPSoCs. We instantiate the framework into a real-world MPSoC platform, targeting Xilinx Vitis-AI as a representative example of a commercial Deep Learning acceleration toolkit for edge environments. We design a comprehensive experimental campaign and apply it to the platform for several convolutional neural networks, each trained on three different datasets. We show that our approach can be used for both hardware- and software-level analysis of a target system. Among other findings, the analysis revealed a suboptimal behavior of the underlying toolkit runtime, involving the utilization of the accelerator cores and the uneven software latency of the support library, influenced by the shapes of the input tensors.

A multi-source change detection algorithm supporting user customization and near real-time deforestation detections

The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current systems. First, alerts often require multiple observations to confirm deforestation, which does not help enforcement if deforestation events are short-lived. Second, the methods can be computationally intensive and often focus on presenting binary detection alerts. Third, results are often reported as static accuracies or temporal lags, with relatively little discussion about how either could be improved for different use-cases. We therefore propose a novel, multisource NRT monitoring algorithm that addresses these issues by quickly achieving high-accuracy detections, using simple data harmonization to combine data sources while yielding probabilities of deforestation, and can be customized to meet management goals where users can explicitly change the algorithm to prioritize latency or accuracy in their detections based on their chosen data inputs. To demonstrate the algorithm's potential, we used combined Landsat-8, Sentinel-2, and Sentinel-1 SAR data in northern Myanmar to monitor areas with contrasting forest types. When testing all possible parameterizations, we achieved comparable results to previous studies, with median false positive rates and false negative rates under 10% and 25%, respectively, and median F1 scores consistently above 75% across the sensor combinations. Median post-data acquisition detection times were faster than other studies and ranged from 2 to 8 days, though the majority were between 2 and 5 days. In attempting to optimize the algorithm for our demonstrated region, we discovered there was no optimal parameterization that simultaneously provided the best accuracy and detection. In addition, we found that including Sentinel-1 data did not improve latency or accuracy, most likely due to relatively high levels of noise in our monsoonal study system that experienced high seasonal precipitation variation. We finally chose an example parameterization to create daily landscape maps of deforestation at 10 m resolution in two regions within the study area (∼60,000 ha). We assert this new, customizable method with the quantification of accuracy and latency trade-offs represents a clear advance for NRT change detection and can improve current operational systems.

Study on Fault Monitoring Technology of Photovoltaic Panel Based on Thermal Infrared and Optical Remote Sensing

Abstract. Rapid access to the operating status of Photovoltaic (PV) panels and troubleshooting can save management and maintenance costs for the development of PV power plants, which is important for PV power plant management and power generation capacity assurance. The use of remote sensing technology to identify the faults of photovoltaic panels has developed rapidly, however, current research usually relies only on a single optical data source to identify and count the area of PV panels in a PV electric field, although there are literature on PV panel fault detection, only the surface fault identification of PV panels is tested, while the internal faults (such as panel bad points or bad lines) cannot be identified because of the limitations of optical remote sensing. In this paper, a photovoltaic panel fault monitoring technology based on multi-source remote sensing is proposed. The optical and thermal infrared hybrid data combined with deep learning technology are used to achieve rapid and accurate fault identification and localization of PV panel arrays. It can not only automatically identify PV panels that are obscured by dust and foreign objects, but also locate PV panels that have bad dots or bad lines, which greatly improves the ability and effectiveness of remote sensing PV panel fault monitoring. The high-resolution unmanned air vehicle (UAV) optical image and thermal infrared image are applied in this experiment. The Mask RCNN algorithm is used to accurately locate and number the photovoltaic panel of the optical image. Then, the fault scene classification model is established for the multi-type fault characteristics of the optical image and thermal infrared image within the panel range, so as to identify five types of faults, such as dust cover, branch cover, bird droppings cover, internal bad points and bad lines of PV panel, which effectively solves the problem that the single optical remote sensing image cannot identify the internal component faults of the photovoltaic panel under normal conditions.

“Image-Spectral” fusion monitoring of small cotton samples nitrogen content based on improved deep forest

Accurate monitoring of nitrogen nutrition is crucial for improving cotton yield and quality, as well as the ecological environment. The mainstream method for monitoring nutrition is to establish traditional machine learning (ML) models using a single data source. However, this approach has limitations such as limited access to feature information, model-fitting problems, and limited generalization. Deep learning (DL), on the other hand, has shown promise in complex nonlinear modeling tasks due to its flexible structure. However, it has its limitations, such as the fact that agronomic sample collection and testing are usually labor- and material-intensive, resulting in sample sizes that are too small to meet its training conditions. Therefore, there is an urgent need for DL models that can effectively integrate features from multiple data sources and accurately monitor crop nitrogen content, especially in scenarios with small samples. In this study, we conducted indoor pot experiments using the cotton variety Xinluzao 53 and subjected it to six nitrogen treatments. The data sources for our analysis included hyperspectral and digital images of the cotton leaves. To enhance representation learning capabilities, we enriched the multi-class base learners within each layer of the deep forest (DF) model and introduced skip connections. These enhancements improved the quality of inversion for both hyperspectral and digital image datasets. We then developed image-spectral fusion models, which combined the DF structure with stacking ensemble learning. Our focus was on three levels of fusion: feature-level fusion, decision-level fusion, and secondary decision-level fusion. This approach aimed to further enhance the accuracy and stability of nitrogen content inversion. The DF model satisfied the training condition for small samples. Compared to traditional ML algorithms and the original DF algorithm, the improved DF model achieved an increase in validation set R2 of 13.4–28.5% and 10.9–14.9%, respectively. These findings highlight the enhanced accuracy and stability of the improved DF model. Additionally, compared to the optimal inversion model using two single data sources, the “Image-Spectral” three-level fusion models exhibited improvements in validation set R2 of 8.6–9.3%, 10.5–11.2%, and 11.8–12.5% for feature-level, decision-level, and secondary decision-level fusion, respectively. The improved DF and three-level fusion model collectively contributed to the increased accuracy of cotton nitrogen content inversion. Among these models, the secondary decision-level fusion model demonstrated the most marked improvement. This methodology provides valuable insights into monitoring crop phenotypic parameters in situations with limited sample sizes.

Multi-sensor Data Analysis for Aerial Image Semantic Segmentation and Vectorization

Abstract. One of the urgent and constantly in demand problems is updating maps. Maps, representing geo-information in vector form, have undoubted advantages in compactness and ”readability” compared to aerial photographs. The issue of maps actuality is critically important for rational urban planning, precision farming, the relevance of the cadastre and other geospatial applications. Various sources of data are used for maps updating, with aerial imagery being the main and rich source of information. Automatic processing of aerial photographs makes it possible to efficiently extract vector information, providing operational monitoring and accounting for changes that have appeared. The presented study addresses the problem of multi sensor information fusion in order to obtain accurate vector information. We use aerial images as a main data source and additionally the data of laser scanning and ground survey to increase performance of automatic image semantic segmentation and vectorization. The proposed framework is demonstrated on the task of forest monitoring.